Feifei
Zhu
,
David E.
Clemmer
and
Jonathan C.
Trinidad
*
Department of Chemistry, Indiana University, 800 Kirkwood Ave., Bloomington, IN 47405, USA. E-mail: trinidad@indiana.edu
First published on 10th November 2016
Determining the affinity between a lectin and its target glycans is an important goal, both for understanding the biological functions of a given lectin as well as enabling the use of that lectin for targeted enrichment of glycosylated species from complex samples. While the overall selectivities of many lectins have been characterized, such studies generally require individually purified lectins and glycans. From these analyses, it is clear that a given lectin does not bind all of its target glycans with the same affinity. Rather, lectins display a continuum of affinities for the range of glycan structures they may encounter. Because of this continuum, it is not straightforward in practice to determine which set of structures will be enriched using a lectin as an affinity reagent. Here we describe the development of glycan affinity chromatography coupled directly to electrospray mass spectrometry, which enables direct analysis of interactions of lectins with both glycans and glycoconjugates from complex mixtures. By observing the elution behavior of individual species, we are able to determine exactly which set of glycoconjugates would be enriched for a given lectin. Furthermore, this approach allows for the direct assessment of affinity constants between an individual lectin and a large number of glycans in a single experiment, which can be conducted using a complex mixture of unpurified glycans of varying concentrations.
Multiple strategies exist to enrich glycosylated proteins/peptides, including lectin-based approaches,16–20 hydrophilic interaction chromatography (HILIC),21–24 and boronate-based chromatography.25,26 The use of immobilized lectin, either in a precipitation-type or chromatographic format, is perhaps the most widely employed glycan enrichment approach. In lectin affinity chromatography (LAC), lectins are immobilized onto a chromatographic matrix such as silica, agarose, or POROS.27 LAC typically employs high ionic strength buffer, and elution of tightly-bound glycosylated species is accomplished by injection of a specific saccharide to competitively displace these species. These procedures require additional washing and desalting steps before MS analysis, and therefore are typically accomplished offline. While offline fractionation is widely used, it can result in potential sample loss.28 In solving this issue, methods have been reported that allow on-line desalting and fractionation of glycopeptide mixtures subsequent to glyco-enrichment.16
We recently published a manuscript describing a WGA-based glycopeptide enrichment approach that allowed for the identification of 2500 unique glycopeptides,29 which was a significant advance in the number of glycopeptides identified in a single experiment. Nevertheless, there remains a substantial need for improved glycopeptide enrichment techniques. In those experiments, it was necessary to run the peptide digest over the lectin column three consecutive times to achieve sufficient separation from non-modified peptides, and the final purified fraction still contained approximately 1/3 non-glycosylated species. A limitation of agarose precipitation or column-based lectin affinity approaches is the difficulty in quantitatively determining the effect of varying wash and elution conditions.30–32 Individual wash or chromatographic fractions can be analyzed for the ratio of non-modified versus glycosylated species, but such an approach is laborious and semi-quantitative at best. This may partially explain the widespread presence of non-glycosylated proteins and peptides that are identified as background in such analyses.
In many cases, the binding affinities of a lectin for a panel of sugar structures have been determined. Such studies often focus on monosaccharides or simple oligosaccharides rather than the range of more complicated glycan structures produced by an organism. Many techniques exist for studying sugar–protein interactions, including surface plasmon resonance,33,34 titration calorimetry,35 frontal affinity chromatography,36–38 capillary affinity electrophoresis,39,40 and lectin microarray techniques.41,42 However, measuring the glycan–lectin binding interactions can be challenging because the interaction is often weak, with association constants less than 105 M−1. In addition, many of the above affinity measurement techniques require determination of precise concentrations of individual glycans, which can be difficult to achieve in a complex mixture.
This work reports the development of affinity chromatography directly coupled to MS for rapid screening of glycopeptides and evaluation of weak affinity glycan–lectin interactions. Determination of peak elution profiles allows for precise determination of binding affinities. Using this approach, we have compared a series of glycopeptides varying in the glycan and/or peptide moiety to investigate their relative contribution to glycopeptide binding. We evaluated several types of resin as suitable matrices for the enrichment and separation, and demonstrated that the methacrylate resin has fewer non-specific interactions than the polystyrene-based resin. This profiling approach allows for direct, quantitative optimization of parameters for glycopeptide enrichment strategies.
Fig. 1 MS spectra of a mixture of glycopeptides from a tryptic digest of ovomucoid, RNase B and α-crystallin. (A) shows the m/z distribution of this sample when directly infused into the mass spectrometer. Minor peaks can be seen for two glycoforms of SIEFGTNISK at m/z = 1299 and 1603. (B) shows the TIC of an online LAC using a WGA-coated POROS column at 0.1 ml min−1 flow rate. The survey scan was summed from 10–20 minutes (1C) and 20–40 minutes (1D). Peaks corresponding to glycopeptides listed in Table 1 are colored red, with the specific glycan structures listed for the most prominent peaks. |
When a column with WGA immobilized on POROS resin was used to separate the peptides, the bulk of the unbound, non-modified peptides eluted between 7 and 20 min (Fig. 1B). In contrast, most glycopeptides had much longer retention times, ranging from 20 to 40 min (Fig. 1C). Fig. 1D demonstrates that the lectin affinity chromatography effectively enriched the glycopeptides in the mixture, leading to significant increases in their MS signals, and the majority of the most intense peaks corresponded to glycopeptides.
Fig. 2 Normalized extracted ion chromatograms (XICs) of non-modified peptides (blue) and glycopeptides (red) obtained from WGA-coated POROS (A) and Tosoh (B) columns. The XICs have been smoothed by adjacent-averaging 30 points per window and background-subtracted based on the TIC. The peptide information is listed in Table 1. Recovery rate for non-modified and glycosylated peptides on the two columns were calculated as a function of fraction start time (C–D). Fold-recovery was also calculated by dividing the relative yield of glycopeptides at that time point by the non-modified peptide relative yield. |
Peptide | z | Peptide sequenceb | Glycoprotein | Glycan type | Glycan formc | Elution time | |
---|---|---|---|---|---|---|---|
number | m/z | ||||||
a A total of 24 glycopeptides and one non-modified peptide were analyzed at a 0.2 ml min−1 flow rate. b For each sequence, the site of glycosylation is indicated by an asterisk. The Uniprot IDs are as follows: α-crystallin (P02489); ovomucoid (P01005); RNase B (P61823); HRP (P00433); fetuin (P12763). c The observed glycans were annotated as follows: N-acetylglucosamine (GlcNAc); mannose (Man); galactose (Gal); N-acetylneuraminic acid (NeuAc); xylose (Xyl); fucose (Fuc). | |||||||
0 | 821.4 | 2 | AIPVSREEKPSSAPSS | α-Crystallin | NA | NA | 8.0 |
1 | 923.0 | 2 | AIPVS*REEKPSSAPSS | α-Crystallin | O-Linked | GlcNAc1 | 10.5 |
2 | 846.4 | 2 | N*LTK | RNAse B | High mannose | Man5GlcNAc2 | 10.8 |
3 | 967.9 | 2 | SRN*LTK | RNAse B | High mannose | Man5GlcNAc2 | 10.8 |
4 | 927.4 | 2 | N*LTK | RNAse B | High mannose | Man6GlcNAc2 | 11.1 |
5 | 1049.0 | 2 | SRN*LTK | RNAse B | High mannose | Man6GlcNAc2 | 11.2 |
6 | 1008.4 | 2 | N*LTK | RNAse B | High mannose | Man7GlcNAc2 | 11.0 |
7 | 1130.0 | 2 | SRN*LTK | RNAse B | High mannose | Man7GlcNAc2 | 11.0 |
8 | 1089.5 | 2 | N*LTK | RNAse B | High mannose | Man8GlcNAc2 | 11.5 |
9 | 1211.0 | 2 | SRN*LTK | RNAse B | High mannose | Man8GlcNAc2 | 11.5 |
10 | 994.4 | 2 | SIEFGTN*ISK | Ovomucoid | Core | Man3GlcNAc2 | 14.8 |
11 | 1299.1 | 2 | SIEFGTN*ISK | Ovomucoid | Hybrid | Man3GlcNAc5 | 18.5 |
12 | 1400.6 | 2 | SIEFGTN*ISK | Ovomucoid | Hybrid | Man3GlcNAc6 | 12.2 |
13 | 1481.6 | 2 | SIEFGTN*ISK | Ovomucoid | Hybrid | Gal1Man3GlcNAc6 | 12.4 |
14 | 1502.1 | 2 | SIEFGTN*ISK | Ovomucoid | Hybrid | Man3GlcNAc7 | 13.3 |
15 | 1603.7 | 2 | SIEFGTN*ISK | Ovomucoid | Hybrid | Man3GlcNAc8 | 12.4 |
16 | 1684.7 | 2 | SIEFGTN*ISK | Ovomucoid | Hybrid | Gal1Man3GlcNAc8 | 12.8 |
17 | 921.9 | 2 | NVGLN*R | HRP | Hybrid | Xyl1Man3Fuc1GlcNAc2 | 8.2 |
18 | 1306.1 | 2 | MGN*ITPLTGTQGQIR | HRP | Hybrid | Xyl1Man3GlcNAc2 | 8.8 |
19 | 1677.2 | 2 | SFAN*STQTFFNAFVEAMDR | HRP | Hybrid | Xyl1Man3Fuc1GlcNAc2 | 10.7 |
20 | 1698.2 | 2 | QLTPTFYDNSCPN*VSNIVR | HRP | Hybrid | Xyl1Man3Fuc1GlcNAc2 | 10.4 |
21 | 1836.4 | 2 | GLIQSDQELFSSPN*ATDTIPLVR | HRP | Hybrid | Xyl1Man3Fuc1GlcNAc2 | 8.2 |
22 | 1470.1 | 4 | VVHAVEVALATFNAESN*GSYLQLVEISR | Fetuin | Complex | NeuAc3Gal3Man3GlcNAc5 | 21.4 |
23 | 1542.9 | 4 | VVHAVEVALATFNAESN*GSYLQLVEISR | Fetuin | Complex | NeuAc4Gal3Man3GlcNAc5 | 30.6 |
24 | 1633.9 | 4 | RPTGEVYDIEIDTLETTCHVLDPTPLAN*CSVR | Fetuin | Complex | NeuAc3Gal3Man3GlcNAc5 | 25.1 |
25 | 1706.2 | 4 | RPTGEVYDIEIDTLETTCHVLDPTPLAN*CSVR | Fetuin | Complex | NeuAc4Gal3Man3GlcNAc5 | 35.5 |
The elution behavior of peptides bearing hybrid glycans displayed a complex behavior. Glycopeptides 10–12, 14, and 15 contain increasing numbers of GlcNAc (Man3GlcNAcx, x = 2, 5, 6, 7, 8, respectively); however, their elution order does not increase directly with the number of GlcNAc residues (Table 1). Glycopeptide 11 containing the glycan Man3GlcNAc5 had the longest retention time of this set, at 18.5 min; either the addition or removal of a GlcNAc residue shortened this value (Table 1). These observations indicate that Man3GlcNAc5 (structures shown in Table S1†) has the highest affinity to WGA among these high-mannose and hybrid type glycans. Most lectins have carbohydrate-recognition domains that tend to form a shallow but relatively well-defined binding pocket to recognize specific glycan chains. It has previously been suggested that the tetrasaccharide GlcNAcβ1-4Manβ1-4GlcNAcβ1-4GlcNAc structural moiety can most efficiently fit into the binding pocket of WGA and that additional modifications to this structural moiety can reduce the binding affinity,44 which is consistent with our current observations. Glycopeptide 10, which contains the N-linked pentasaccharide core structure, does not have the full structural moiety due to the absence of the bisecting GlcNAc (Table S1†), and it possesses a decreased affinity compared to glycopeptide 11. In contrast, glycopeptides 12, 14, and 15 possess this tetrasaccharide structural moiety, but also contain additional GlcNAc residues that may sterically hinder this structure from fitting into the WGA binding pocket, which resulted in decreased binding affinities. These observations reinforce the notion that the binding affinity of a glycopeptide depends on the overall glycan structure that comes into contact with the lectin rather than simply the composition of the glycan. Experimental data regarding the relative binding affinities such as we can obtain may prove useful to evaluate the accuracy of in silico structural modeling of lectin binding proteins and their corresponding ligands.
The glycopeptides derived from HRP contain xylose (and fucose) modified versions of the N-linked pentasaccharide core (Table S1†). These glycopeptides eluted earlier from the WGA column relative to the other glycopeptides. The most significant difference between these and other glycopeptides tested is the presence of both fucose and xylose. Our previous manuscript analyzing glycopeptides from complex samples resulted in the identification of many glycopeptides which appeared to have fucose-modified core pentasaccharides.29 It is therefore most likely that the addition of xylose prevents the optimal binding between the pentasaccharide and WGA, thereby significantly reducing the glycan binding affinity.
The glycopeptides from fetuin, which contain complex-type glycans, show much higher retention times than the other glycopeptides (Table 1). These glycopeptides all contain terminal NeuAc residues, which, in addition to GlcNAc, have been demonstrated to interact relatively strongly with WGA.45,46 The better retention of these NeuAc-containing peptides relative to peptide 11 confirms that WGA can interact with multiple glycan structural elements.
Glycopeptides 19 and 20 were also shown to have greater retention times than glycopeptide 21 despite the fact that they have the same glycan and have amino acid sequences of similar length. Comparison of the retention profiles for glycopeptides 22–25 from fetuin also suggests some degree of non-specific interactions. A close examination reveals that peptides containing aromatic residues, i.e., F, Y, and W, have longer retention times on the POROS resin material. The POROS resin is made of cross-linked polystyrene–divinylbenzene, which contains large arrays of aromatic rings that can interact with aromatic amino acid residues. We suspect that peptides containing aromatic residues show increased retention times, with multiple aromatic residues resulting in longer retention time shifts. Thus, it is important to consider these non-specific interactions when using POROS as the immobilization matrix.
Overall, the Tosoh resin provided the best separation between non-modified peptides and glycosylated peptides. Non-glycosylated peptides had peak elution times between 6.6 and 9.7 minutes. Glycosylated peptides had peak elution times between 10.2 and 21.7 minutes. To estimate the relative enrichment capabilities of the POROS and Tosoh resins, we calculated the overall recoveries that would be obtained, from having collected the remaining column eluant at various start times (Fig. 2C and D). We estimated overall non-modified and glycosylated peptide recoveries by averaging the respective XICs in Fig. 2A and B. To estimate the fold-recovery at any time point, we divided the relative yield of glycopeptides at that time point by the non-modified peptide relative yield. These estimates will depend partly on the set of peptide elution profiles chosen, but using a range of distinct peptides and glycopeptides should give a relative approximation of the enrichment efficiency.
For the POROS resin, achieving a 95% yield of the 13 glycopeptides would require collecting the eluant starting at 9.1 minutes. This would also capture 22% of the non-modified peptides, corresponding to 4.3-fold enrichment. For the Tosoh resin, achieving a 95% glycopeptide yield would require collecting starting at 10.6 minutes, which would also capture 9.4% of the non-glycopeptides, corresponding to a 10-fold enrichment. The estimated recovery rates of these peptides in different retention time windows (to mimic offline fractionation) are shown in Table S2.† The Tosoh resin provided better overall separation between the non-modified and glycosylated peptides, which is a key factor to consider in offline fractionations. Despite the lower overall enrichment, the POROS resin was judged better for on-line LAC-MS because the narrow elution profiles gave more intense MS signals, and the samples being analyzed were not of high enough complexity such that the glycopeptides suffered significant ion suppression.
Our LAC-MS approach can also be used to calculate association constants, without the requirement that other approaches have to obtain individual glycans in their purified forms and/or at known concentrations. For lectin–glycan binding, the Ka value can be expressed as Ka = (tR − t0)/(t0C[L]). In this equation, tR and t0 refer to the analyte retention time and void volume time, respectively; C is a constant reflecting the physical characteristics of the matrix including the porosity; and [L] is the effective lectin concentration. The product C[L] can be empirically determined for a given lectin column using a reference standard with a known Ka. The Ka value for WGA interacting with β-GlcNAc is 2 × 102 M−1.52,53 For species measured on the same lectin column, Ka is proportional to the adjusted retention time (tR − t0)/t0. This allows us to calculate Ka values for the isolated glycan and demonstrate them to be in the range from 1 × 102 to 6 × 102 M−1 (Table 2). It is worthwhile to note that the equation is valid provided that the analyte has a concentration c0 (at injection) such that c0Ka ≪ 1.54 This condition is satisfied for our experiments since the Ka value for the glycan–lectin binding is usually on the order of 102–104 M−1,35,38,39 and the glycans used in this study were estimated to be on the μM range. While the Ka values for complex glycans reported in Table 2 have not been reported using alternative methods to calculate affinities, our observation that the Ka of α GlcNAc is approximately twice that of β GlcNAc is consistent with previously reported literature,52,53 thereby providing independent support for the accuracy of our calculations.
Glycan | t R | t R − t0 | K a (M−1) |
---|---|---|---|
a A Ka value of 2 × 102 M−1 was used for the interaction between WGA and β-GlcNAc.52,53 | |||
β-GlcNAc | 15.1 | 8.9 | 2 × 102a |
α-GlcNAc | 21.2 | 15.0 | 3 × 102 |
Man3GlcNAc2 | 23.0 | 16.8 | 4 × 102 |
Man3GlcNAc5 | 31.3 | 25.1 | 6 × 102 |
Gal1Man3GlcNAc6 | 15.6 | 9.4 | 2 × 102 |
Man3GlcNAc8 | 11.5 | 5.3 | 1 × 102 |
Gal1Man3GlcNAc8 | 12.6 | 6.4 | 1 × 102 |
Fig. 4 The adjusted retention times of the glycopeptides on different lectin-coated POROS columns at the flow rate of 0.2 ml min−1. These glycopeptides are from four proteins that contain distinct types of glycan structures: high-mannose glycans from RNase B, xylose (and fucose) modified core pentasaccharide from HRP, hybrid glycans from ovomucoid, and complex glycans from fetuin (Table 1). Glycopeptides are shown with m/z labels and associated glycan structures. The * symbols for the high-mannose glycopeptides from RNase B indicate that the retention times of these glycopeptides on the Con A-coated column exceeded the time course of the experiment. |
Con A and LCA are known to capture mannose-rich carbohydrates.57,58 The high-mannose glycopeptides from RNase B were well retained by the LCA lectins (Fig. 4). In fact, the RNase B high-mannose glycopeptides were not eluted off the Con A column during the one hour gradient due to their tight binding affinities, with Ka values around 105 M−1.59,60 These glycopeptides were subsequently recovered by injection of methyl-α-mannose. The relative affinities of the different types of glycopeptides to the Con A lectin are in agreement with previously published values.34,47,61,62 LCA was found to bind complex type glycopeptides, in addition to its reported specificity for the chitobiose core structure.63,64 The xylose-modified core glycopeptides from HRP were well retained by both Con A and LCA, with slight variation in the relative affinities for individual species, suggesting the existence of somewhat minor lectin–peptide interactions. Neither lectin shows significant affinity for the hybrid-type glycopeptides from ovomucoid.
SBA and APA lectins show very similar binding specificities. As galactose-specific lectins, they have been shown to bind Gal1Man3GlcNAc6 and Gal1Man3GlcNAc8, respectively.65–67 We demonstrate that these lectins bind galactose-containing complex glycans from fetuin. In fact, these fetuin glycopeptides are more strongly retained than either Gal1Man3GlcNAc6 or Gal1Man3GlcNAc8.
Immobilization of WGA on Tosoh resin allowed better separation between glycosylated and non-glycosylated peptides. This could be due to higher overall protein binding and/or a higher percentage of bound WGA remaining functional with respect to glycopeptide binding. Initial lectin immobilization was performed in the presence of the respective target glycan in an attempt to limit covalent modification of the substrate recognition region. Over the course of these and previous glycopeptide experiments, we have constructed four different columns using WGA immobilized on POROS. We have assayed the binding ability of these columns by measuring their retention of GlcNAc as measured by UV and seen vary little column-to-column variability in binding ability.52 While equivalent binding conditions were used for both resins, it is possible that a higher effective WGA surface concentration was immobilized on the Tosoh resin or that effects such as resin particle size and pore size allow more interaction events between the glycopeptides and immobilized WGA. For both the Tosoh and POROS columns, the elution peaks for peptides and glycopeptides are significantly broader than would be obtained by reverse phase LC-MS. Therefore, for deep glycopeptide profiling from complex mixtures, offline LAC (using conditions determined by online LAC) followed by orthogonal LC-MS of enriched fractions would likely provide much higher depth of coverage compared to online LAC. For analysis of single proteins or simple mixtures, online LAC may be a more straightforward approach that allows determination of a protein's glycoforms as well as providing information regarding the glycan–substrate interaction.
Multidimensional lectin chromatography or serial lectin chromatography is often used in order to cover a broad range of glycomic features. However, the choice of the lectin combination in these approaches is mainly qualitative and empirical. Using online LAC-MS, the global affinities of the lectin combination towards different glycan structures can be measured in a systematic manner. Importantly, these measurements can be obtained in a multiplexed fashion, with a single LAC-MS analysis capable of providing affinity data on many individual glycans in a complex mixture. In addition to plant-derived lectins, our approach can characterize binding specificities for other lectins or glycan-binding proteins in general. We also envision its general utility for characterizing interactions between pairs of proteins or between proteins and small molecules.
Our comparison of the glycan-binding affinities of five different lectins was particularly informative. A key step in most glycoproteomic experiments is the initial enrichment of a broad pool of glycopeptides. To account for the fact that individual lectins are relatively specific for specific saccharides, multi-lectin enrichment approaches have been adopted.19,20 Here we provide direct evidence that WGA is able to enrich a wide range of glycan structures, including complex, hybrid and high-mannose N-glycans. This is likely due to that fact that not only can WGA bind GlcNAc and NeuAc, but it can interact with the core pentasaccharide which is common to all N-glycans. However, the presence of xylose appears to inhibit recognition of WGA for the core pentasaccharide. As such, while WGA has the broadest glycan affinity of the five lectins tested, it cannot be used as a universal affinity reagent for any type of glycopeptide. For those peptides, a mannose or galactose-specific lectin would be more appropriate.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c6an02043g |
This journal is © The Royal Society of Chemistry 2017 |